gators.feature_generation.PolynomialObjectFeatures¶
-
class
gators.feature_generation.
PolynomialObjectFeatures
(columns: List[str], degree=2)[source]¶ Create new columns based on object columns addition.
- Parameters
- theta_vecList[float]
List of columns.
- degreeint, default = 2
The degree of polynomial. The default of degree of 2 will produce A * A, B * B, and A * B from features A and B.
Examples
Imports and initialization:
>>> from gators.feature_generation import PolynomialObjectFeatures >>> obj = PolynomialObjectFeatures(columns=['A', 'B', 'C'], degree=3)
The fit, transform, and fit_transform methods accept:
dask dataframes:
>>> import dask.dataframe as dd >>> import pandas as pd >>> import dask.dataframe as dd >>> import pandas as pd >>> X = dd.from_pandas(pd.DataFrame({'A': [None, 'b', 'c'], 'B': ['z', 'a', 'a'], 'C': ['c', 'd', 'd']}), npartitions=1)
koalas dataframes:
>>> import databricks.koalas as ks >>> X = ks.DataFrame({'A': [None, 'b', 'c'], 'B': ['z', 'a', 'a'], 'C': ['c', 'd', 'd']})
and pandas dataframes:
>>> import pandas as pd >>> X = pd.DataFrame({'A': [None, 'b', 'c'], 'B': ['z', 'a', 'a'], 'C': ['c', 'd', 'd']})
The result is a transformed dataframe belonging to the same dataframe library.
>>> obj.fit_transform(X) A B C A__x__B A__x__C B__x__C A__x__B__x__C 0 None z c z c zc zc 1 b a d ba bd ad bad 2 c a d ca cd ad cad
Independly of the dataframe library used to fit the transformer, the tranform_numpy method only accepts NumPy arrays and returns a transformed NumPy array. Note that this transformer should only be used when the number of rows is small e.g. in real-time environment.
>>> X = pd.DataFrame({'A': [None, 'b', 'c'], 'B': ['z', 'a', 'a'], 'C': ['c', 'd', 'd']}) >>> _ = obj.fit(X) >>> obj.transform_numpy(X.to_numpy()) array([[None, 'z', 'c', 'z', 'c', 'zc', 'zc'], ['b', 'a', 'd', 'ba', 'bd', 'ad', 'bad'], ['c', 'a', 'd', 'ca', 'cd', 'ad', 'cad']], dtype=object)
-
fit
(X: Union[pd.DataFrame, ks.DataFrame, dd.DataFrame], y: Union[pd.Series, ks.Series, dd.Series] = None) → gators.feature_generation.polynomial_object_features.PolynomialObjectFeatures[source]¶ Fit the dataframe X.
- Parameters
- XDataFrame.
Input dataframe. y (np.ndarray, optional): labels. Defaults to None.
- Returns
- selfPolynomialObjectFeatures
Instance of itself.
-
transform
(X: Union[pd.DataFrame, ks.DataFrame, dd.DataFrame]) → Union[pd.DataFrame, ks.DataFrame, dd.DataFrame][source]¶ Transform the dataframe X.
- Parameters
- XDataFrame.
Input dataframe.
- Returns
- XDataFrame
Transformed dataframe.
-
transform_numpy
(X: numpy.ndarray) → numpy.ndarray[source]¶ Transform the array X.
- Parameters
- Xnp.ndarray
Input array.
- Returns
- Xnp.ndarray
Transformed array.
-
static
check_array
(X: numpy.ndarray)¶ Validate array.
- Parameters
- Xnp.ndarray
Array.
-
check_array_is_numerics
(X: numpy.ndarray)¶ Check if array is only numerics.
- Parameters
- Xnp.ndarray
Array.
-
static
check_binary_target
(X: Union[pd.DataFrame, ks.DataFrame, dd.DataFrame], y: Union[pd.Series, ks.Series, dd.Series])¶ Raise an error if the target is not binary.
- Parameters
- ySeries
Target values.
-
static
check_dataframe
(X: Union[pd.DataFrame, ks.DataFrame, dd.DataFrame])¶ Validate dataframe.
- Parameters
- XDataFrame
Dataframe.
-
static
check_dataframe_contains_numerics
(X: Union[pd.DataFrame, ks.DataFrame, dd.DataFrame])¶ Check if dataframe is only numerics.
- Parameters
- XDataFrame
Dataframe.
-
static
check_dataframe_is_numerics
(X: Union[pd.DataFrame, ks.DataFrame, dd.DataFrame])¶ Check if dataframe is only numerics.
- Parameters
- XDataFrame
Dataframe.
-
check_dataframe_with_objects
(X: Union[pd.DataFrame, ks.DataFrame, dd.DataFrame])¶ Check if dataframe contains object columns.
- Parameters
- XDataFrame
Dataframe.
-
check_datatype
(dtype, accepted_dtypes)¶ Check if dataframe is only numerics.
- Parameters
- XDataFrame
Dataframe.
-
static
check_multiclass_target
(y: Union[pd.Series, ks.Series, dd.Series])¶ Raise an error if the target is not discrete.
- Parameters
- ySeries
Target values.
-
check_nans
(X: Union[pd.DataFrame, ks.DataFrame, dd.DataFrame], columns: List[str])¶ Raise an error if X contains NaN values.
- Parameters
- XDataFrame
Dataframe.
- theta_vecList[float]
List of columns.
-
static
check_regression_target
(y: Union[pd.Series, ks.Series, dd.Series])¶ Raise an error if the target is not discrete.
- Parameters
- ySeries
Target values.
-
static
check_target
(X: Union[pd.DataFrame, ks.DataFrame, dd.DataFrame], y: Union[pd.Series, ks.Series, dd.Series])¶ Validate target.
- Parameters
- XDataFrame
Dataframe.
- ySeries
Target values.
-
fit_transform
(X: Union[pd.DataFrame, ks.DataFrame, dd.DataFrame], y: Union[pd.Series, ks.Series, dd.Series] = None) → Union[pd.DataFrame, ks.DataFrame, dd.DataFrame]¶ Fit and Transform the dataframe X.
- Parameters
- XDataFrame.
Input dataframe.
- ySeries, default None.
Input target.
- Returns
- XDataFrame
Transformed dataframe.
-
static
get_column_names
(inplace: bool, columns: List[str], suffix: str)¶ Return the names of the modified columns.
- Parameters
- inplacebool
If True return columns. If False return columns__suffix.
- columnsList[str]
List of columns.
- suffixstr
Suffix used if inplace is False.
- Returns
- List[str]
List of column names.
-
get_params
(deep=True)¶ Get parameters for this estimator.
- Parameters
- deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators.
- Returns
- paramsdict
Parameter names mapped to their values.
-
set_params
(**params)¶ Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as
Pipeline
). The latter have parameters of the form<component>__<parameter>
so that it’s possible to update each component of a nested object.- Parameters
- **paramsdict
Estimator parameters.
- Returns
- selfestimator instance
Estimator instance.